Channel equalization for multiuser chaotic communications systems

Ciftci, Mahmut; Williams, D.B.

doi:10.1109/icassp.2002.5743994

Cited by 10 publications

(3 citation statements)

References 3 publications

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“…Initial values of the equalizer coefficients were g 11 = g 22 = δ[n − 10] . A 50-point rectangular window was used to calculate the cross-correlation in (18) for δ 1 = −24, δ 2 = 25 . The impulse responses h ij [n], j = 1, 2. at convergence are illustrated in Figure 2, from which it can be seen that the condition h 12 [n] = h 21 [n] = 0 is almost satisfied.…”

Section: Resultsmentioning

confidence: 99%

“…However, a SISO model is assumed for the channel in most works [12][13][14][15][16][17]. For the MIMO channel case, there are only a few nonblind algorithms developed by using training sequences [18,19]. In previous studies [18,20], a trellis diagram is substituted for a multiuser communication system.…”

Section: Introductionmentioning

confidence: 99%

“…For the MIMO channel case, there are only a few nonblind algorithms developed by using training sequences [18,19]. In previous studies [18,20], a trellis diagram is substituted for a multiuser communication system. The corresponding trellis diagram is obtained by using a special representation for chaos signals called symbolic dynamic representation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive blind equalization for a MIMO chaotic communication system

Çetinel¹,

Vural²

2020

Turk J Elec Eng & Comp Sci

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There exist few blind solutions for chaotic MIMO channel equalization. In this work, a chaotic MIMO channel equalization framework is proposed. The objective function to be minimized in the proposed solution is obtained by adopting the objective function developed for chaotic SISO channel equalization. Furthermore, an optimum filter that minimizes the proposed cost function is designed to recover chaotic input signals assuming that the channel is known. The stationary point of the adaptive solution is equal to the optimal filter if the adaptive filter coefficients change sufficiently slowly. The adaptive solution is contrasted with the optimum filter in terms of mean-square error and bit error rate performances. In addition, the proposed solution reconstructs chaotic input signals at the same time. Consequently, it can be applied to multiple signal separation problems as well.

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Section: Resultsmentioning

confidence: 99%